Addressing Uncertainty in Flood Hazard Mapping under a Bayesian Approach
Publication: Journal of Hydrologic Engineering
Volume 29, Issue 3
Abstract
Flood mapping is a crucial tool for assisting urban planning and emergency response plans and, consequently, preventing or reducing the risks associated with flood disasters. However, in developing countries that often lack or have limited data, to produce such maps is a challenging task. When topographic data are lacking, digital elevation models (DEMs) derived from the Shuttle Radar Topography Mission (SRTM) are frequently used as a freely available surrogate, albeit with additional uncertainty. This work presents an integrated framework to investigate flood inundation areas using a Bayesian approach, while including steps for calibrating SRTM data and determining the river bathymetry below the WSE. A flood event in the Itaqui municipality, in the state of Rio Grande do Sul, southern Brazil is used to demonstrate the proposed framework. Findings suggest benefits in using calibrated SRTM DEMs for flood mapping regardless of whether flood inundation areas were derived directly from projections of WSEs on the terrain or based on hydraulic simulations. Results further highlight the potential of using a Bayesian approach to improve quality and reliability of flood hazards maps, especially in regions that lack topographic data.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request including the Bayesian R programming code, water surface elevation, and streamflow records, and ground control points.
References
ANA (Agencia Nacional de Águas). 2022. “SNIRH—Hidroweb.” Accessed January 11, 2022. http://www.snirh.gov.br/hidroweb.
Araújo, P. V. D. N., V. E. Amaro, A. V. C. Alcoforado, and A. L. S. dos Santos. 2018. “Vertical accuracy and calibration of digital elevation models (DEMs) for the piranhas-assu river Basin, Rio Grande Do Norte, Brazil.” Anuário Inst. Geociências 41 (1): 351–364. https://doi.org/10.11137/2018_1_351_364.
Araújo, P. V. N., V. E. Amaro, L. S. Aguiar, C. C. Lima, and A. B. Lopes. 2021. “Tidal flood area mapping in the face of climate change scenarios: Case study in a tropical estuary in the Brazilian semi-arid region.” Nat. Hazards Earth Syst. Sci. 21 (11): 3353–3366. https://doi.org/10.5194/nhess-21-3353-2021.
Araújo, P. V. N., V. E. Amaro, R. M. Silva, and A. B. Lopes. 2016. “On the use of SRTM and altimetry data for flood modeling in data-sparse regions.” Water Resour. Res. 52 (1): 2901–2918. https://doi.org/10.1002/2015WR017967.
Araújo, P. V. N., V. E. Amaro, R. M. Silva, and A. B. Lopes. 2019. “Delimitation of flood areas based on a calibrated a DEM and geoprocessing: Case study on the Uruguay River, Itaqui, southern Brazil.” Nat. Hazards Earth Syst. Sci. 19 (1): 237–250. https://doi.org/10.5194/nhess-19-237-2019.
Brasil. 2007. Manual for hydropower inventory studies of River Basins. Brasília, Brazil: Ministry of Mines and Energy.
Brasil. 2013. “Portaria Interministerial Ministério da Integração Nacional/Ministério das Cidades n. 01 de 24 de Julho de 2013.” Accessed December 20, 2023. https://antigo.mdr.gov.br/images/stories/ArquivosSNH/ArquivosPDF/Portarias/PORTARIA_INTERMINISTERIAL_N_1_24.07.2013_MCIDADES_E_MI_PMCMV_CALAMIDADE.pdf.
Brody, S. D., R. Blessing, A. Sebastian, and P. Bedient. 2013. “Delineating the reality of flood risk and loss in Southeast Texas.” Nat. Hazard. Rev. 14 (2): 89–97. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000091.
Brody, S. D., Y. Lee, and W. E. Highfield. 2017. “Household adjustment to flood risk: A survey of coastal residents in Texas and Florida, United States.” Disasters 41 (3): 566–586. https://doi.org/10.1111/disa.12216.
Castellarin, A., G. di Baldassarre, P. D. Bates, and A. Brath. 2009. “Optimal cross-sectional spacing in Preissmann scheme 1D hydrodynamic models.” J. Hydraul. Eng. 135 (2): 96–105. https://doi.org/10.1061/(ASCE)0733-9429(2009)135:2(96).
Cook, A., and V. Merwade. 2009. “Effect of topographic data, geometric configuration and modeling approach on flood inundation mapping.” J. Hydrol. 377 (1–2): 131–142. https://doi.org/10.1016/j.jhydrol.2009.08.015.
CRED and UNISDR (The Centre for Research on the Epidemiology of Disasters and the United Nations Office for Disaster Risk Reduction). 2015. The human cost of weather related disasters 1995—2015. Geneva: CRED and UNISDR.
Cunnane, C. 1989. Statistical distributions for flood frequency analysis 128. Geneva: World Meteorological Organization.
Demir, V., and O. Kisi. 2016. “Flood hazard mapping by using geographic information system and hydraulic model: Mert River, Samsun, Turkey.” Adv. Meteorol. 2016 (Feb): 1–9. https://doi.org/10.1155/2016/4891015.
Dhiman, R., R. Vishnu Radhan, T. I. Eldho, and A. Inamdar. 2019. “Flood risk and adaptation in Indian coastal cities: Recent scenarios.” Appl. Water Sci. 9 (Feb): 1–16. https://doi.org/10.1007/s13201-018-0881-9.
Domeneghetti, A. 2016. “On the use of SRTM and altimetry data for flood modeling in data-sparse regions.” Water Resour. Res. 52 (4): 2901–2918. https://doi.org/10.1002/2015WR017967.
Ekeu-Wei, I. T., and G. A. Blackburn. 2018. “Applications of open-access remotely sensed data for flood modelling and mapping in developing regions.” Hydrology 5 (3): 39. https://doi.org/10.3390/hydrology5030039.
Eletrobrás. 1985. Metodologia para regionalização de vazões. Rio de Janeiro, Brasil: Eletrobrás.
England, J. F., T. A. Cohn, B. A. Faber, J. R. Stedinger, W. O. Thomas, A. G. Veilleux, J. E. Kiang, and R. R. Mason. 2019. Guidelines for determining flood flow frequency. Reston, VA: USGS.
Farr, T. G., et al. 2007. “The shuttle radar topography mission.” Rev. Geophys. 45 (2): RG2004. https://doi.org/10.1029/2005RG000183.
Filliben, J. J. 1975. “The probability plot correlation coefficient test for normality.” Technometrics 17 (1): 111–117. https://doi.org/10.1080/00401706.1975.10489279.
Griffis, V. W., and J. R. Stedinger. 2007. “Log-Pearson type 3 distribution and its application in flood frequency analysis. I: Distribution characteristics.” J. Hydrol. Eng. 12 (5): 482–491. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:5(482).
Hosking, J. R. M. 1986. The theory of probability-weighted moments. New York: IBM T.J. Watson Research Center.
Hosking, J. R. M. 1990. “L-moments: Analysis and estimation of distributions using linear combinations of order statistics.” J. R. Stat. Soc. Ser. B 52 (1): 105–124. https://doi.org/10.1111/j.2517-6161.1990.tb01775.x.
Jalayer, F., et al. 2014. “Probabilistic GIS-based method for delineation of urban flooding risk hotspots.” Nat. Hazards 73 (Sep): 975–1001. https://doi.org/10.1007/s11069-014-1119-2.
Knebl, M. R., Z. L. Yang, K. Hutchison, and D. R. Maidment. 2005. “Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: A case study for the San Antonio River Basin Summer 2002 storm event.” J. Environ. Manage. 75 (4): 325–336. https://doi.org/10.1016/j.jenvman.2004.11.024.
Komi, K., J. Neal, M. A. Trigg, and B. Diekkrüger. 2017. “Modelling of flood hazard extent in data sparse areas: A case study of the Oti River basin, West Africa.” J. Hydrol.: Reg. Stud. 10 (Apr): 122–132. https://doi.org/10.1016/j.ejrh.2017.03.001.
Langat, P. K., L. Kumar, and R. Koech. 2019. “Identification of the most suitable probability distribution models for maximum, minimum, and mean streamflow.” Water 11 (4): 734. https://doi.org/10.3390/w11040734.
Le Coz, J., B. Renard, L. Bonnifait, F. Branger, and R. le Boursicaud. 2014. “Combining hydraulic knowledge and uncertain gaugings in the estimation of hydrometric rating curves: A Bayesian approach.” J. Hydrol. 509 (Feb): 573–587. https://doi.org/10.1016/j.jhydrol.2013.11.016.
Leopold, L. B., and T. Maddock. 1953. The hydraulic geometry of stream channels and some physiographic implications. Washington, DC: US Government Printing Office.
Manfreda, S., M. di Leo, and A. Sole. 2011. “Detection of flood-prone areas using digital elevation models.” J. Hydrol. Eng. 16 (10): 781–790. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000367.
Mersel, M. K., L. C. Smith, K. M. Andreadis, and M. T. Durand. 2013. “Estimation of river depth from remotely sensed hydraulic relationships.” Water Resour. Res. 49 (6): 3165–3179. https://doi.org/10.1002/wrcr.20176.
Merwade, V., F. Olivera, M. Arabi, and S. Edleman. 2008. “Uncertainty in flood inundation mapping: Current issues and future directions.” J. Hydrol. Eng. 13 (10): 608–620. https://doi.org/10.1061/(ASCE)1084-0699(2008)13:7(608).
Nardi, F., E. R. Vivoni, and S. Grimaldi. 2006. “Investigating a floodplain scaling relation using a hydrogeomorphic delineation method.” Water Resour. Res. 42 (9): 1–15. https://doi.org/10.1029/2005WR004155.
Osorio, A. L. N. A. 2017. “Modelo Bayesiano Completo para análise de frequência de cheias com incorporação do conhecimento hidráulico na modelagem das incertezas na curva- chave.” Dissertação de Mestrado em Tecnologia Ambiental e Recursos Hídricos, Publicação PTARH.DM-196/17, Departamento de Engenharia Civil e Ambiental, Universidade de Brasília.
Osorio, A. L. N. A., C. G. Rampinelli, and D. S. Reis. 2018. “A Bayesian approach to incorporate imprecise information on hydraulic knowledge in a river reach and assess prediction uncertainties in streamflow data.” In Proc., World Environmental and Water Resources Congress. Reston, VA: ASCE.
Ozturk, U., E. Bozzolan, E. A. Holcombe, R. Shukla, F. Pianosi, and T. Wagener. 2022. “How climate change and unplanned urban sprawl bring more landslides.” Nature 608 (7922): 262–265. https://doi.org/10.1038/d41586-022-02141-9.
Patel, D. P., J. A. Ramirez, P. K. Srivastava, M. Bray, and D. Han. 2017. “Assessment of flood inundation mapping of Surat city by coupled 1D/2D hydrodynamic modeling: A case application of the new HEC-RAS 5.” Nat. Hazards 89 (Oct): 93–130. https://doi.org/10.1007/s11069-017-2956-6.
Pedrozo-Acuña, A., J. P. Rodríguez-Rincón, M. Arganis-Juárez, R. Domínguez-Mora, and F. J. González Villareal. 2015. “Estimation of probabilistic flood inundation maps for an extreme event: Pánuco River, México.” J Flood Risk Manage. 8 (2): 177–192. https://doi.org/10.1111/jfr3.12067.
Petersen-Øverleir, A., and T. Reitan. 2005. “Objective segmentation in compound rating curves.” J. Hydrol. 311 (1–4): 188–201. https://doi.org/10.1016/j.jhydrol.2005.01.016.
Pourali, S. H., C. Arrowsmith, N. Chrisman, A. A. Matkan, and D. Mitchell. 2016. “Topography wetness index application in flood-risk-based land use planning.” Appl. Spatial Anal. Policy 9 (Mar): 39–54. https://doi.org/10.1007/s12061-014-9130-2.
Rampinelli, C. G., I. Knack, and T. Smith. 2020. “Flood mapping uncertainty from a restoration perspective: A practical case study.” Water 12 (7): 1948. https://doi.org/10.3390/w12071948.
Reis, D. S., and J. R. Stedinger. 2005. “Bayesian MCMC flood frequency analysis with historical information.” J. Hydrol. 313 (1–2): 97–116. https://doi.org/10.1016/j.jhydrol.2005.02.028.
Ries, K. G., M. Y. Crouse, J. B. Atkins, R. Dusenbury, M. Gray, M. E. Jennings, W. H. Kirby, H. C. Riggs, V. B. Sauer, and W. O. Thomas. 2002. The national flood frequency program, version 3: A computer program for estimating magnitude and frequency of floods for ungauged sites. Reston, VA: USGS.
Rowe, T. J., and J. C. Smithers. 2018. “Continuous simulation modelling for design flood estimation—A South African perspective and recommendations.” Water SA 44 (4): 691–705. https://doi.org/10.4314/wsa.v44i4.18.
Roy, D. P., et al. 2014. “Landsat-8: Science and product vision for terrestrial global change research.” Remote Sens. Environ. 145 (Apr): 154–172. https://doi.org/10.1016/j.rse.2014.02.001.
Salinas, J. L., A. Castellarin, A. Viglione, S. Kohnová, and T. R. Kjeldsen. 2014. “Regional parent flood frequency distributions in Europe—Part 1: Is the GEV model suitable as a pan-European parent?” Hydrol. Earth Syst. Sci. 18 (11): 4381–4389. https://doi.org/10.5194/hess-18-4381-2014.
Samela, C., S. Manfreda, F. de Paola, M. Giugni, A. Sole, and M. Fiorentino. 2016. “DEM-based approaches for the delineation of flood-prone areas in an ungauged basin in Africa.” J. Hydrol. Eng. 21 (2): 06015010. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001272.
Sanders, B. F. 2007. “Evaluation of on-line DEMs for flood inundation modeling.” Adv. Water Resour. 30 (4): 1831–1843. https://doi.org/10.1016/j.advwatres.2007.02.005.
Saueressig, S. R. 2012. “Zoneamento das Áreas de Risco a Inundação da Área Urbana de Itaqui-RS.” Dissertação de Mestrado em geografia, Programa de Pós Graduação em Geografia, Universidade Federal de Santa Maria.
Saueressig, S. R., and L. E. S. Robaina. 2015. “Zoneamento Das Áreas De Risco a Inundação Da Área Urbana De Itaqui-RS.” Boletim Gaúcho de Geografia. Accessed December 10, 2022. http://www.seer.ufrgs.br/index.php/bgg/article/view/41397/34042.
Schumann, G., P. Matgen, M. E. J. Cutler, A. Black, L. Hoffmann, and L. Pfister. 2008. “Comparison of remotely sensed water stages from LiDAR, topographic contours and SRTM.” ISPRS J. Photogramm. Remote Sens. 63 (3): 283–296. https://doi.org/10.1016/j.isprsjprs.2007.09.004.
Sevruk, B., and H. Geiger. 1981. Selection of distribution types for extremes of precipitation, selection of distribution types for extremes of precipitation. Geneva: World Meteorological Organization.
Silva, R. M., V. S. Moreira, and A. B. Lopes. 2017. “Geodetic method to obtain a digital elevation model associated to the Brazilian Geodetic System.” Int. J. Eng. Technol. Res. 7 (9): 14–17.
Speckhann, G. A., P. L. Borges Chaffe, R. Fabris Goerl, J. J. de Abreu, and J. A. Altamirano Flores. 2018. “Flood hazard mapping in Southern Brazil: A combination of flow frequency analysis and the HAND model.” Hydrol. Sci. J. 63 (1): 87–100. https://doi.org/10.1080/02626667.2017.1409896.
Stephens, T. A., and B. P. Bledsoe. 2020. “Probabilistic mapping of flood hazards: Depicting uncertainty in streamflow, land use, and geomorphic adjustment.” Anthropocene 29 (Mar): 100231. https://doi.org/10.1016/j.ancene.2019.100231.
te Chow, V., D. R. Maidment, and L. W. Mays. 1988. Applied hydrology. New York: McGraw-Hill.
Teng, J., A. J. Jakeman, J. Vaze, B. F. W. Croke, D. Dutta, and S. Kim. 2017. “Flood inundation modelling: A review of methods, recent advances and uncertainty analysis.” Environ. Modell. Software 90 (Apr): 201–216. https://doi.org/10.1016/j.envsoft.2017.01.006.
Tung, Y.-K., B.-C. Yen, and C. S. Melching. 2006. Hydrosystems engineering reliability assessment and risk analysis. New York: McGraw-Hill.
UNISDR (The United Nations Office for Disaster Risk Reduction). 2015. Sendai framework for disaster risk reduction 2015-2030. Geneva: UNISDR.
USACE. 2019. River analysis system-HEC-RAS 5.0.7. Davis, CA: Hydrologic Engineering Center.
USGS. 2022. “Earth explorer.” Accessed December 20, 2022. https://earthexplorer.usgs.gov/gov.
van Emmerik, T., G. Mulder, D. Eilander, M. Piet, and H. Savenije. 2015. “Predicting the ungauged basin: Model validation and realism assessment.” Front. Earth Sci. 3 (Oct): 62. https://doi.org/10.3389/feart.2015.00062.
Vogel, R. M. 1986. “The probability plot correlation coefficient test for the normal, lognormal, and Gumbel distributional hypotheses.” Water Resour. Res. 22 (Apr): 587–590. https://doi.org/10.1029/WR022i004p00587.
Vogel, R. W., and D. E. McMartin. 1991. “Probability plot goodness-of-fit and skewness estimation procedures for the Pearson type 3 distribution.” Water Resour. Res. 27 (12): 3149–3158. https://doi.org/10.1029/91WR02116.
Vrugt, J. A. 2016. “Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation.” Environ. Modell. Software 75 (Jan): 273–316. https://doi.org/10.1016/j.envsoft.2015.08.013.
Vrugt, J. A., C. J. F. Ter Braak, M. P. Clark, J. M. Hyman, and B. A. Robinson. 2008. “Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation.” Water Resour. Res. 44 (12): 1–15. https://doi.org/10.1029/2007WR006720.
Vrugt, J. A., C. J. F. Ter Braak, C. G. H. Diks, B. A. Robinson, J. M. Hyman, and D. Higdon. 2009. “Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling.” Int. J. Nonlinear Sci. Numer. Simul. 10 (3): 273–290. https://doi.org/10.1515/IJNSNS.2009.10.3.273.
WBG (World Bank Group) and GFDRR (Global Facility for Disaster Reduction and Recovery). 2018. Assessment of the state of hydrological services in developing countries. Washington, DC: World Bank.
Webster, V. L., and J. R. Stedinger. 2019. “Flood frequency analysis in the United States.” In Statistical analysis of hydrologic variables: Methods and applications, 233–268. Reston, VA: ASCE.
Westra, S., H. J. Fowler, J. P. Evans, L. V. Alexander, P. Berg, F. Johnson, E. J. Kendon, G. Lenderink, and N. M. Roberts. 2014. “Future changes to the intensity and frequency of short-duration extreme rainfall.” Rev. Geophys. 52 (3): 522–555. https://doi.org/10.1002/2014RG000464.
Whiteaker, T. L., O. Robayo, D. R. Maidment, and D. Obenour. 2006. “From a NEXRAD rainfall map to a flood inundation map.” J. Hydrol. Eng. 11 (1): 37–45. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:1(37).
Yan, K., G. di Baldassarre, and D. P. Solomatine. 2013. “Exploring the potential of SRTM topographic data for flood inundation modelling under uncertainty.” J. Hydroinf. 15 (3): 849–861. https://doi.org/10.2166/hydro.2013.137.
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Journal of Hydrologic Engineering
Volume 29 • Issue 3 • June 2024
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© 2024 American Society of Civil Engineers.
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Received: Jul 3, 2023
Accepted: Nov 14, 2023
Published online: Feb 23, 2024
Published in print: Jun 1, 2024
Discussion open until: Jul 23, 2024
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